Design of a predictive control tuned by an evolutionary algorithm applied to a nonlinear conical tank level
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Abstract
The control of nonlinear dynamic systems, such as the liquid level in a conical tank, poses a critical challenge in various military applications, particularly for handling heavy liquids and slurries. This paper presents the design of a Model Predictive Control (MPC) algorithm that uses Particle Swarm Optimization (PSO) and Least Squares (LS) to control the liquid level in a conical tank. A prototype was built in a laboratory at the Universidad de las Fuerzas Armadas ESPE. The research highlights the limitations of traditional control methods, which fail to achieve a fast transient response and often cause overshoots. In the face of this problem, the incorporation of tuned evolutionary algorithms for MPC control is proposed to improve control performance for nonlinear systems.
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